class_docstr: Filters out edges if their incident nodes are not in any cluster.
  
  .. math::
          \mathbf{A}^{\prime} &= \mathbf{A}_{\mathbf{i},\mathbf{i}},
  
  where :math:`\mathbf{i}` denotes the set of retained nodes.
  It is assumed that each cluster contains only one node.
code: def forward(
      self,
      select_output: SelectOutput,
      edge_index: Tensor,
      edge_attr: Optional[Tensor] = None,
      batch: Optional[Tensor] = None,
  ) -> ConnectOutput:
      if (
          not torch.jit.is_scripting()
          and select_output.num_clusters
          != select_output.cluster_index.size(0)
      ):
          raise ValueError(
              f"'{self.__class__.__name__}' requires each "
              f"cluster to contain only one node"
          )
  
      edge_index, edge_attr = filter_adj(
          edge_index,
          edge_attr,
          select_output.node_index,
          select_output.cluster_index,
          num_nodes=select_output.num_nodes,
      )
      batch = self.get_pooled_batch(select_output, batch)
  
      return ConnectOutput(edge_index, edge_attr, batch)
  
file_path: pytorch_geometric/torch_geometric/nn/pool/connect/filter_edges.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torch_geometric.nn.pool.connect import Connect, ConnectOutput
  from torch_geometric.nn.pool.select import SelectOutput
  from torch_geometric.utils.num_nodes import maybe_num_nodes
label_desc: Use image node_id 1 for calling the FilterEdges obj's underlying member method code with example usage: obj.forward(select_output, edge_index, edge_attr, batch) and returns: ConnectOutput